Quantification of SHM Benefits and Costs Using the Theory of Value of Information

Recent decades have seen remarkable advances in structural health monitoring (SHM) technologies, both sensing systems and data processing methods. However, a questions which is only now starting to be seriously addressed is whether adopting those SHM systems can indeed be economically justified, given that SHM data always exhibit inherent stochastic uncertainty while their collection and processing incur costs. The theory of value of information (VoI) can quantify the expected benefits of SHM data after, or even before, SHM implementation, supporting rational decisions. While calculating VoI, the prior theoretical risk of structural failure is updated using the probabilities of failure adjusted using SHM data. The net VoI is then the reduction in the structural failure risk when SHM data becomes available from which the cost of SHM is subtracted. In this paper, SHM will first be discussed in a broader context of asset management as an enabling technology. Then, the quantification of VoI of SHM data will be illustrated with applications to a prestressed concrete bridge, for which long-term strain monitoring campaigns enabled an accurate assessment of the net benefit of the financial expenditure required for it structural upgrading, and optimized fatigue progression monitoring in an offshore wind turbine. Finally, broader prioritizations of the applications of SHM to entire stocks of structural assets, such as bridges in a large transportation system or decommissioned offshore and subsea oil and gas installations, will be discussed from the VoI perspective.